Neural Network Technologies in Drug Registration: Computerised Analysis of Documents and Real-Time Systems

INTRODUCTION. Current methods of handling medicine regulatory documents are associated with high time cost (40-60% of labour hours), frequent documentation errors, and limited data interoperability. Neural network technologies have enabled the enhanced document preparation and a transition to full automation of the registration dossier life cycle.

AIM. This study aimed to evaluate the possibility of using artificial intelligence (AI) systems in preparing a drug registration dossier.

DISCUSSION. Natural language processing (NLP) models demonstrate high efficiency for the regulatory documentation. Named entity recognition (NER) systems with 89–96% entity extraction accuracy rate reduces the processing (preparation and quality review) time for documents within electronic Common Technical Document (eCTD) by 64%, but face limitations in interpreting morphologically complex terms and require annotated datasets. Without additional fine-tuning, generative models such as GPT-4, are prone to generating inaccurate facts when used in the Retrieval-Augmented Generation (RAG) architecture. Predictive systems based on graph neural networks and XGBoost ensembles demonstrate high accuracy (ROC AUC up to 0.88) when predicting drug approval; however, they cannot interpret decisions and data systematic biases. Developing document-centric platforms with NLP reduces the dossier preparation time by 60%, still, implementing an automated procedure for generating dossier sections requires an expert verification.

CONCLUSIONS. The concept of integrated AI systems proves its effectiveness by reducing the document handling time by manufacturers and increasing the accuracy of decisions, which in turn speeds up the market launch of medicinal products. The prospects of introducing digital technologies are associated with overcoming definitions differences through unified ontologies. Practical implementation requires the development of unified standards for the validation of AI algorithms and adaptive systems.

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